02 Visualizing main results
Sebastian Sauer
2022-04-11 15:23:55
- 1 Setup
- 2 Import data
- 3 Compare progression for metric
variables
- 3.1 Comparing metric variables with regard to progress (bad/good)
- 3.2 alter
- 3.3 af
- 3.4 sat_percent
- 3.5 hf
- 3.6 bp_sys
- 3.7 bp_dia
- 3.8 gcs
- 3.9 temp
- 3.10 esi
- 3.11 q_sofa
- 3.12 ven_bga_p_co2_mm_hg
- 3.13 vbga_p_o2_mm_hg
- 3.14 ven_laktat_mmol_l
- 3.15 fi_o2_umgerechnet_percent
- 3.16 leukos_nl
- 3.17 thrombos_nl
- 3.18 neutros_nl
- 3.19 lymphos_nl
- 3.20 nlr
- 3.21 quick_percent
- 3.22 harnstoff_mg_dl
- 3.23 crea_mg_dl
- 3.24 e_gfr_ml_min
- 3.25 bilirubin_ges_mg_d_l
- 3.26 ast_u_l
- 3.27 alt_u_l
- 3.28 ldh_u_l
- 3.29 crp_mg_d_l
- 3.30 procalcitonin_ng_m_l
- 3.31 ro
- 3.32 bg
- 4 Comparing nominal variables with regard to p rogress (bad/good)
1 Setup
library("tidyverse")
library("here")
library("assertthat")
library("VIM")
library("janitor")
library("ggstatsplot")
library("glue")
library("gt")
library("rstatix")
library("broom")
library("futile.logger")
library("gt")
library("infer")
conflicted::conflict_prefer("discard", "purrr")
conflicted::conflict_prefer("filter", "dplyr")flog.appender(appender.file("covid-icu.log"))
flog.threshold(INFO)
flog.info("STARTING script 02")
flog.info("Logger inited")source(here("src", "01-constants.R"))
source(here("src", "funs", "prepare-data.R"))
source(here("src", "funs", "helper-funs.R"))
source(here("src", "funs", "prop_NA.R"))
source(here("src", "funs", "compare_progression.R"))
source(here("src", "funs", "is_binary.R"))2 Import data
if (params$dataset == "small") {
d <- read_csv(here("data", "processed", "data-small-prepared.csv"))
} else {
d <- read_csv(here("data", "processed", "data-prepared.csv"))
}3 Compare progression for metric variables
names_binary_vars <-
d %>%
keep(is_binary, na.rm = TRUE) %>%
names()
names_binary_vars## [1] "verlauf"
## [2] "geschlecht"
## [3] "supportive_o2"
## [4] "fieber"
## [5] "husten"
## [6] "dyspnoe"
## [7] "geruchs_geschmacksverlust"
## [8] "kopfschm"
## [9] "gliederschm"
## [10] "abgeschlagenheit"
## [11] "schuttelfrost"
## [12] "halsschmerzen"
## [13] "diarrho_emesis"
## [14] "mutation"
## [15] "dm"
## [16] "adipositas"
## [17] "art_hypert"
## [18] "chron_herzerkr"
## [19] "asthma_bronchiale"
## [20] "nicht_asthmat_lungenerkr"
## [21] "chron_nierenerkrankung"
## [22] "nikotinabusus"
## [23] "maligne_vorerkrankung"
## [24] "ns_hf"
## [25] "niv"
## [26] "beatmung_imv"
## [27] "ecmo"
## [28] "dialyse"
## [29] "tod"
## [30] "sepsis"
## [31] "blutungen"
## [32] "resp_insuff"
## [33] "pneumonie_bakterielle_virale_fungale_superinfektion"
## [34] "kardiale_beteiligung_tara_vhf_mi"
## [35] "akutes_nierenversagen"names_metric_vars <-
d %>%
keep(negate(~ is_binary(., na.rm = TRUE))) %>%
names()
names_metric_vars## [1] "id" "alter"
## [3] "af" "sat_percent"
## [5] "hf" "bp_sys"
## [7] "bp_dia" "gcs"
## [9] "temp" "esi"
## [11] "q_sofa" "ven_bga_p_co2_mm_hg"
## [13] "vbga_p_o2_mm_hg" "ven_laktat_mmol_l"
## [15] "fi_o2_umgerechnet_percent" "leukos_nl"
## [17] "thrombos_nl" "neutros_nl"
## [19] "lymphos_nl" "nlr"
## [21] "quick_percent" "harnstoff_mg_dl"
## [23] "crea_mg_dl" "e_gfr_ml_min"
## [25] "bilirubin_ges_mg_d_l" "ast_u_l"
## [27] "alt_u_l" "ldh_u_l"
## [29] "crp_mg_d_l" "procalcitonin_ng_m_l"
## [31] "ro" "bg"3.1 Comparing metric variables with regard to progress (bad/good)
List of metric variables to be compared with regard to progress (bad/good):
cols_to_be_compared_metric <-
names_metric_vars %>%
discard(~ .x %in% c("fallnummer", "verlauf", "id"))
cols_to_be_compared_metric## [1] "alter" "af"
## [3] "sat_percent" "hf"
## [5] "bp_sys" "bp_dia"
## [7] "gcs" "temp"
## [9] "esi" "q_sofa"
## [11] "ven_bga_p_co2_mm_hg" "vbga_p_o2_mm_hg"
## [13] "ven_laktat_mmol_l" "fi_o2_umgerechnet_percent"
## [15] "leukos_nl" "thrombos_nl"
## [17] "neutros_nl" "lymphos_nl"
## [19] "nlr" "quick_percent"
## [21] "harnstoff_mg_dl" "crea_mg_dl"
## [23] "e_gfr_ml_min" "bilirubin_ges_mg_d_l"
## [25] "ast_u_l" "alt_u_l"
## [27] "ldh_u_l" "crp_mg_d_l"
## [29] "procalcitonin_ng_m_l" "ro"
## [31] "bg"cols_to_be_compared_metric %>%
map(~ compare_progression_metric(d,
group = verlauf,
var = .))3.2 alter
Starting analysis for variable: alter
Grouping variable: verlauf
3.2.1 Violin plot
3.2.2 Histogram
3.2.3 Table
3.2.4 Inference: t-Test between the two groups:
3.2.5 Visualizing differences between the two groups:
End of analysis for variable: alter
3.3 af
Starting analysis for variable: af
Grouping variable: verlauf
3.3.1 Violin plot
3.3.2 Histogram
3.3.3 Table
3.3.4 Inference: t-Test between the two groups:
3.3.5 Visualizing differences between the two groups:
End of analysis for variable: af
3.4 sat_percent
Starting analysis for variable: sat_percent
Grouping variable: verlauf
3.4.1 Violin plot
3.4.2 Histogram
3.4.3 Table
3.4.4 Inference: t-Test between the two groups:
3.4.5 Visualizing differences between the two groups:
End of analysis for variable: sat_percent
3.5 hf
Starting analysis for variable: hf
Grouping variable: verlauf
3.5.1 Violin plot
3.5.2 Histogram
3.5.3 Table
3.5.4 Inference: t-Test between the two groups:
3.5.5 Visualizing differences between the two groups:
End of analysis for variable: hf
3.6 bp_sys
Starting analysis for variable: bp_sys
Grouping variable: verlauf
3.6.1 Violin plot
3.6.2 Histogram
3.6.3 Table
3.6.4 Inference: t-Test between the two groups:
3.6.5 Visualizing differences between the two groups:
End of analysis for variable: bp_sys
3.7 bp_dia
Starting analysis for variable: bp_dia
Grouping variable: verlauf
3.7.1 Violin plot
3.7.2 Histogram
3.7.3 Table
3.7.4 Inference: t-Test between the two groups:
3.7.5 Visualizing differences between the two groups:
End of analysis for variable: bp_dia
3.8 gcs
Starting analysis for variable: gcs
Grouping variable: verlauf
3.8.1 Violin plot
3.8.2 Histogram
3.8.3 Table
3.8.4 Inference: t-Test between the two groups:
3.8.5 Visualizing differences between the two groups:
End of analysis for variable: gcs
3.9 temp
Starting analysis for variable: temp
Grouping variable: verlauf
3.9.1 Violin plot
3.9.2 Histogram
3.9.3 Table
3.9.4 Inference: t-Test between the two groups:
3.9.5 Visualizing differences between the two groups:
End of analysis for variable: temp
3.10 esi
Starting analysis for variable: esi
Grouping variable: verlauf
3.10.1 Violin plot
3.10.2 Histogram
3.10.3 Table
3.10.4 Inference: t-Test between the two groups:
3.10.5 Visualizing differences between the two groups:
End of analysis for variable: esi
3.11 q_sofa
Starting analysis for variable: q_sofa
Grouping variable: verlauf
3.11.1 Violin plot
3.11.2 Histogram
3.11.3 Table
3.11.4 Inference: t-Test between the two groups:
3.11.5 Visualizing differences between the two groups:
End of analysis for variable: q_sofa
3.12 ven_bga_p_co2_mm_hg
Starting analysis for variable: ven_bga_p_co2_mm_hg
Grouping variable: verlauf
3.12.1 Violin plot
3.12.2 Histogram
3.12.3 Table
3.12.4 Inference: t-Test between the two groups:
3.12.5 Visualizing differences between the two groups:
End of analysis for variable: ven_bga_p_co2_mm_hg
3.13 vbga_p_o2_mm_hg
Starting analysis for variable: vbga_p_o2_mm_hg
Grouping variable: verlauf
3.13.1 Violin plot
3.13.2 Histogram
3.13.3 Table
3.13.4 Inference: t-Test between the two groups:
3.13.5 Visualizing differences between the two groups:
End of analysis for variable: vbga_p_o2_mm_hg
3.14 ven_laktat_mmol_l
Starting analysis for variable: ven_laktat_mmol_l
Grouping variable: verlauf
3.14.1 Violin plot
3.14.2 Histogram
3.14.3 Table
3.14.4 Inference: t-Test between the two groups:
3.14.5 Visualizing differences between the two groups:
End of analysis for variable: ven_laktat_mmol_l
3.15 fi_o2_umgerechnet_percent
Starting analysis for variable: fi_o2_umgerechnet_percent
Grouping variable: verlauf
3.15.1 Violin plot
3.15.2 Histogram
3.15.3 Table
3.15.4 Inference: t-Test between the two groups:
3.15.5 Visualizing differences between the two groups:
End of analysis for variable: fi_o2_umgerechnet_percent
3.16 leukos_nl
Starting analysis for variable: leukos_nl
Grouping variable: verlauf
3.16.1 Violin plot
3.16.2 Histogram
3.16.3 Table
3.16.4 Inference: t-Test between the two groups:
3.16.5 Visualizing differences between the two groups:
End of analysis for variable: leukos_nl
3.17 thrombos_nl
Starting analysis for variable: thrombos_nl
Grouping variable: verlauf
3.17.1 Violin plot
3.17.2 Histogram
3.17.3 Table
3.17.4 Inference: t-Test between the two groups:
3.17.5 Visualizing differences between the two groups:
End of analysis for variable: thrombos_nl
3.18 neutros_nl
Starting analysis for variable: neutros_nl
Grouping variable: verlauf
3.18.1 Violin plot
3.18.2 Histogram
3.18.3 Table
3.18.4 Inference: t-Test between the two groups:
3.18.5 Visualizing differences between the two groups:
End of analysis for variable: neutros_nl
3.19 lymphos_nl
Starting analysis for variable: lymphos_nl
Grouping variable: verlauf
3.19.1 Violin plot
3.19.2 Histogram
3.19.3 Table
3.19.4 Inference: t-Test between the two groups:
3.19.5 Visualizing differences between the two groups:
End of analysis for variable: lymphos_nl
3.20 nlr
Starting analysis for variable: nlr
Grouping variable: verlauf
3.20.1 Violin plot
3.20.2 Histogram
3.20.3 Table
3.20.4 Inference: t-Test between the two groups:
3.20.5 Visualizing differences between the two groups:
End of analysis for variable: nlr
3.21 quick_percent
Starting analysis for variable: quick_percent
Grouping variable: verlauf
3.21.1 Violin plot
3.21.2 Histogram
3.21.3 Table
3.21.4 Inference: t-Test between the two groups:
3.21.5 Visualizing differences between the two groups:
End of analysis for variable: quick_percent
3.22 harnstoff_mg_dl
Starting analysis for variable: harnstoff_mg_dl
Grouping variable: verlauf
3.22.1 Violin plot
3.22.2 Histogram
3.22.3 Table
3.22.4 Inference: t-Test between the two groups:
3.22.5 Visualizing differences between the two groups:
End of analysis for variable: harnstoff_mg_dl
3.23 crea_mg_dl
Starting analysis for variable: crea_mg_dl
Grouping variable: verlauf
3.23.1 Violin plot
3.23.2 Histogram
3.23.3 Table
3.23.4 Inference: t-Test between the two groups:
3.23.5 Visualizing differences between the two groups:
End of analysis for variable: crea_mg_dl
3.24 e_gfr_ml_min
Starting analysis for variable: e_gfr_ml_min
Grouping variable: verlauf
3.24.1 Violin plot
3.24.2 Histogram
3.24.3 Table
3.24.4 Inference: t-Test between the two groups:
3.24.5 Visualizing differences between the two groups:
End of analysis for variable: e_gfr_ml_min
3.25 bilirubin_ges_mg_d_l
Starting analysis for variable: bilirubin_ges_mg_d_l
Grouping variable: verlauf
3.25.1 Violin plot
3.25.2 Histogram
3.25.3 Table
3.25.4 Inference: t-Test between the two groups:
3.25.5 Visualizing differences between the two groups:
End of analysis for variable: bilirubin_ges_mg_d_l
3.26 ast_u_l
Starting analysis for variable: ast_u_l
Grouping variable: verlauf
3.26.1 Violin plot
3.26.2 Histogram
3.26.3 Table
3.26.4 Inference: t-Test between the two groups:
3.26.5 Visualizing differences between the two groups:
End of analysis for variable: ast_u_l
3.27 alt_u_l
Starting analysis for variable: alt_u_l
Grouping variable: verlauf
3.27.1 Violin plot
3.27.2 Histogram
3.27.3 Table
3.27.4 Inference: t-Test between the two groups:
3.27.5 Visualizing differences between the two groups:
End of analysis for variable: alt_u_l
3.28 ldh_u_l
Starting analysis for variable: ldh_u_l
Grouping variable: verlauf
3.28.1 Violin plot
3.28.2 Histogram
3.28.3 Table
3.28.4 Inference: t-Test between the two groups:
3.28.5 Visualizing differences between the two groups:
End of analysis for variable: ldh_u_l
3.29 crp_mg_d_l
Starting analysis for variable: crp_mg_d_l
Grouping variable: verlauf
3.29.1 Violin plot
3.29.2 Histogram
3.29.3 Table
3.29.4 Inference: t-Test between the two groups:
3.29.5 Visualizing differences between the two groups:
End of analysis for variable: crp_mg_d_l
3.30 procalcitonin_ng_m_l
Starting analysis for variable: procalcitonin_ng_m_l
Grouping variable: verlauf
3.30.1 Violin plot
3.30.2 Histogram
3.30.3 Table
3.30.4 Inference: t-Test between the two groups:
3.30.5 Visualizing differences between the two groups:
End of analysis for variable: procalcitonin_ng_m_l
3.31 ro
Starting analysis for variable: ro
Grouping variable: verlauf
3.31.1 Violin plot
3.31.2 Histogram
3.31.3 Table
3.31.4 Inference: t-Test between the two groups:
3.31.5 Visualizing differences between the two groups:
End of analysis for variable: ro
3.32 bg
Starting analysis for variable: bg
Grouping variable: verlauf
3.32.1 Violin plot
3.32.2 Histogram
3.32.3 Table
3.32.4 Inference: t-Test between the two groups:
3.32.5 Visualizing differences between the two groups:
End of analysis for variable: bg
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4 Comparing nominal variables with regard to p rogress (bad/good)
cols_to_be_compared_binary <-
names_binary_vars %>%
discard(~ .x %in% c("fallnr", "verlauf"))d_factor <-
d %>%
select(verlauf, all_of(cols_to_be_compared_binary)) %>%
mutate(across(everything(),
factor))4.1 Vis (pie charts)
ggpiestats(data = d, x = verlauf, y = ns_hf, bf.message = FALSE, title = "ns_hf")
ggpiestats(data = d, x = verlauf, y = niv, bf.message = FALSE, title = "niv")
ggpiestats(data = d, x = verlauf, y = resp_insuff, bf.message = FALSE, title = "resp_insuff")
ggpiestats(data = d, x = verlauf, y = beatmung_imv, bf.message = FALSE, title = "beatmung_imv")
ggpiestats(data = d, x = verlauf, y = pneumonie_bakterielle_virale_fungale_superinfektion, bf.message = FALSE, title = "pneumonie_bakterielle_virale_fungale_superinfektion")
ggpiestats(data = d, x = verlauf, y = sepsis, bf.message = FALSE, title = "sepsis")
ggpiestats(data = d, x = verlauf, y = kardiale_beteiligung_tara_vhf_mi, bf.message = FALSE, title = "kardiale_beteiligung_tara_vhf_mi")
ggpiestats(data = d, x = verlauf, y = tod, bf.message = FALSE, title = "tod")
ggpiestats(data = d, x = verlauf, y = dyspnoe, bf.message = FALSE, title = "dyspnoe")
ggpiestats(data = d, x = verlauf, y = nikotinabusus, bf.message = FALSE, title = "nikotinabusus")
ggpiestats(data = d, x = verlauf, y = art_hypert, bf.message = FALSE, title = "art_hypert")
ggpiestats(data = d, x = verlauf, y = dm, bf.message = FALSE, title = "dm")
ggpiestats(data = d, x = verlauf, y = geschlecht, bf.message = FALSE, title = "geschlecht")
ggpiestats(data = d, x = verlauf, y = kopfschm, bf.message = FALSE, title = "kopfschm")
ggpiestats(data = d, x = verlauf, y = adipositas, bf.message = FALSE, title = "adipositas")